Overview

Dataset statistics

Number of variables19
Number of observations30697
Missing cells55284
Missing cells (%)9.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.7 MiB
Average record size in memory535.2 B

Variable types

Numeric11
Boolean1
Categorical7

Alerts

home_away has a high cardinality: 74 distinct values High cardinality
type_of_shot has a high cardinality: 57 distinct values High cardinality
location_y is highly correlated with distance_of_shot and 1 other fieldsHigh correlation
remaining_min is highly correlated with remaining_min_1High correlation
power_of_shot is highly correlated with power_of_shot_1High correlation
remaining_sec is highly correlated with remaining_sec_1High correlation
distance_of_shot is highly correlated with location_y and 1 other fieldsHigh correlation
remaining_min_1 is highly correlated with remaining_minHigh correlation
power_of_shot_1 is highly correlated with power_of_shotHigh correlation
remaining_sec_1 is highly correlated with remaining_secHigh correlation
distance_of_shot_1 is highly correlated with location_y and 1 other fieldsHigh correlation
location_y is highly correlated with distance_of_shotHigh correlation
distance_of_shot is highly correlated with location_yHigh correlation
location_y is highly correlated with distance_of_shotHigh correlation
remaining_min is highly correlated with remaining_min_1High correlation
power_of_shot is highly correlated with power_of_shot_1High correlation
remaining_sec is highly correlated with remaining_sec_1High correlation
distance_of_shot is highly correlated with location_y and 1 other fieldsHigh correlation
remaining_min_1 is highly correlated with remaining_minHigh correlation
power_of_shot_1 is highly correlated with power_of_shotHigh correlation
remaining_sec_1 is highly correlated with remaining_secHigh correlation
distance_of_shot_1 is highly correlated with distance_of_shotHigh correlation
location_x is highly correlated with distance_of_shot and 6 other fieldsHigh correlation
location_y is highly correlated with distance_of_shot and 5 other fieldsHigh correlation
remaining_sec is highly correlated with remaining_sec_1High correlation
distance_of_shot is highly correlated with location_x and 7 other fieldsHigh correlation
area_of_shot is highly correlated with location_x and 7 other fieldsHigh correlation
shot_basics is highly correlated with location_x and 7 other fieldsHigh correlation
range_of_shot is highly correlated with location_x and 7 other fieldsHigh correlation
home_away is highly correlated with lat_lngHigh correlation
lat_lng is highly correlated with home_awayHigh correlation
type_of_shot is highly correlated with location_x and 5 other fieldsHigh correlation
type_of_combined_shot is highly correlated with location_x and 6 other fieldsHigh correlation
remaining_sec_1 is highly correlated with remaining_secHigh correlation
distance_of_shot_1 is highly correlated with location_x and 7 other fieldsHigh correlation
location_x has 1461 (4.8%) missing values Missing
location_y has 1540 (5.0%) missing values Missing
remaining_min has 1562 (5.1%) missing values Missing
power_of_shot has 1486 (4.8%) missing values Missing
remaining_sec has 1594 (5.2%) missing values Missing
distance_of_shot has 1567 (5.1%) missing values Missing
area_of_shot has 1502 (4.9%) missing values Missing
shot_basics has 1575 (5.1%) missing values Missing
range_of_shot has 1564 (5.1%) missing values Missing
home_away has 1497 (4.9%) missing values Missing
lat_lng has 1565 (5.1%) missing values Missing
type_of_shot has 15280 (49.8%) missing values Missing
type_of_combined_shot has 15417 (50.2%) missing values Missing
remaining_min_1 has 1535 (5.0%) missing values Missing
power_of_shot_1 has 1539 (5.0%) missing values Missing
knockout_match_1 has 1493 (4.9%) missing values Missing
remaining_sec_1 has 1539 (5.0%) missing values Missing
distance_of_shot_1 has 1568 (5.1%) missing values Missing
location_x has 5195 (16.9%) zeros Zeros
location_y has 5324 (17.3%) zeros Zeros
remaining_min has 3681 (12.0%) zeros Zeros
remaining_sec has 931 (3.0%) zeros Zeros
remaining_min_1 has 2921 (9.5%) zeros Zeros
knockout_match_1 has 19831 (64.6%) zeros Zeros
remaining_sec_1 has 747 (2.4%) zeros Zeros

Reproduction

Analysis started2022-02-08 10:22:55.877425
Analysis finished2022-02-08 10:23:18.093121
Duration22.22 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

location_x
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct488
Distinct (%)1.7%
Missing1461
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean7.383876043
Minimum-250
Maximum248
Zeros5195
Zeros (%)16.9%
Negative10981
Negative (%)35.8%
Memory size479.6 KiB
2022-02-08T15:53:18.669387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-250
5-th percentile-177
Q1-68
median0
Q395
95-th percentile182
Maximum248
Range498
Interquartile range (IQR)163

Descriptive statistics

Standard deviation110.2630492
Coefficient of variation (CV)14.93294966
Kurtosis-0.6831823168
Mean7.383876043
Median Absolute Deviation (MAD)85
Skewness-0.08754995724
Sum215875
Variance12157.94003
MonotonicityNot monotonic
2022-02-08T15:53:18.807343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05195
 
16.9%
1256
 
0.8%
138143
 
0.5%
159142
 
0.5%
-2139
 
0.5%
136137
 
0.4%
144136
 
0.4%
108135
 
0.4%
6130
 
0.4%
-8130
 
0.4%
Other values (478)22693
73.9%
(Missing)1461
 
4.8%
ValueCountFrequency (%)
-2501
 
< 0.1%
-2481
 
< 0.1%
-2463
 
< 0.1%
-2452
 
< 0.1%
-2442
 
< 0.1%
-2434
 
< 0.1%
-2425
< 0.1%
-24110
< 0.1%
-24012
< 0.1%
-2395
< 0.1%
ValueCountFrequency (%)
2485
 
< 0.1%
2462
 
< 0.1%
2452
 
< 0.1%
2443
 
< 0.1%
2427
 
< 0.1%
2418
< 0.1%
2407
 
< 0.1%
2396
 
< 0.1%
23812
< 0.1%
23718
0.1%

location_y
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct450
Distinct (%)1.5%
Missing1540
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean91.1269335
Minimum-44
Maximum791
Zeros5324
Zeros (%)17.3%
Negative1447
Negative (%)4.7%
Memory size479.6 KiB
2022-02-08T15:53:18.937578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-44
5-th percentile0
Q14
median74
Q3160
95-th percentile241
Maximum791
Range835
Interquartile range (IQR)156

Descriptive statistics

Standard deviation87.67639516
Coefficient of variation (CV)0.9621348134
Kurtosis1.166423204
Mean91.1269335
Median Absolute Deviation (MAD)74
Skewness0.8040843553
Sum2656988
Variance7687.150268
MonotonicityNot monotonic
2022-02-08T15:53:19.068744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05324
 
17.3%
7239
 
0.8%
12215
 
0.7%
3207
 
0.7%
15196
 
0.6%
17196
 
0.6%
156194
 
0.6%
26182
 
0.6%
23181
 
0.6%
61177
 
0.6%
Other values (440)22046
71.8%
(Missing)1540
 
5.0%
ValueCountFrequency (%)
-442
 
< 0.1%
-431
 
< 0.1%
-422
 
< 0.1%
-402
 
< 0.1%
-392
 
< 0.1%
-382
 
< 0.1%
-373
< 0.1%
-365
< 0.1%
-355
< 0.1%
-343
< 0.1%
ValueCountFrequency (%)
7911
< 0.1%
7731
< 0.1%
7412
< 0.1%
7281
< 0.1%
7111
< 0.1%
7021
< 0.1%
6971
< 0.1%
6961
< 0.1%
6791
< 0.1%
6641
< 0.1%

remaining_min
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing1562
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean4.883233225
Minimum0
Maximum11
Zeros3681
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size479.6 KiB
2022-02-08T15:53:19.212470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.452532713
Coefficient of variation (CV)0.7070177798
Kurtosis-1.164098439
Mean4.883233225
Median Absolute Deviation (MAD)3
Skewness0.1998925127
Sum142273
Variance11.91998213
MonotonicityNot monotonic
2022-02-08T15:53:19.302367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
03681
12.0%
42709
8.8%
32698
8.8%
22688
8.8%
12600
8.5%
52508
8.2%
62256
7.3%
72094
6.8%
92089
6.8%
82028
6.6%
Other values (2)3784
12.3%
ValueCountFrequency (%)
03681
12.0%
12600
8.5%
22688
8.8%
32698
8.8%
42709
8.8%
52508
8.2%
62256
7.3%
72094
6.8%
82028
6.6%
92089
6.8%
ValueCountFrequency (%)
111768
5.8%
102016
6.6%
92089
6.8%
82028
6.6%
72094
6.8%
62256
7.3%
52508
8.2%
42709
8.8%
32698
8.8%
22688
8.8%

power_of_shot
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)< 0.1%
Missing1486
Missing (%)4.8%
Infinite0
Infinite (%)0.0%
Mean2.519359146
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size479.6 KiB
2022-02-08T15:53:19.391117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q33
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.153975941
Coefficient of variation (CV)0.4580434445
Kurtosis-1.164876715
Mean2.519359146
Median Absolute Deviation (MAD)1
Skewness0.05634692367
Sum73593
Variance1.331660473
MonotonicityNot monotonic
2022-02-08T15:53:19.471586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
37885
25.7%
17659
25.0%
46910
22.5%
26399
20.8%
5314
 
1.0%
637
 
0.1%
77
 
< 0.1%
(Missing)1486
 
4.8%
ValueCountFrequency (%)
17659
25.0%
26399
20.8%
37885
25.7%
46910
22.5%
5314
 
1.0%
637
 
0.1%
77
 
< 0.1%
ValueCountFrequency (%)
77
 
< 0.1%
637
 
0.1%
5314
 
1.0%
46910
22.5%
37885
25.7%
26399
20.8%
17659
25.0%

remaining_sec
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct60
Distinct (%)0.2%
Missing1594
Missing (%)5.2%
Infinite0
Infinite (%)0.0%
Mean28.32938185
Minimum0
Maximum59
Zeros931
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size479.6 KiB
2022-02-08T15:53:19.589575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q113
median28
Q343
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.47066279
Coefficient of variation (CV)0.6166976351
Kurtosis-1.183681156
Mean28.32938185
Median Absolute Deviation (MAD)15
Skewness0.03444775101
Sum824470
Variance305.2240583
MonotonicityNot monotonic
2022-02-08T15:53:19.723038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0931
 
3.0%
1620
 
2.0%
2572
 
1.9%
28531
 
1.7%
4530
 
1.7%
32525
 
1.7%
5523
 
1.7%
36522
 
1.7%
25520
 
1.7%
41516
 
1.7%
Other values (50)23313
75.9%
(Missing)1594
 
5.2%
ValueCountFrequency (%)
0931
3.0%
1620
2.0%
2572
1.9%
3494
1.6%
4530
1.7%
5523
1.7%
6456
1.5%
7465
1.5%
8494
1.6%
9445
1.4%
ValueCountFrequency (%)
59440
1.4%
58422
1.4%
57477
1.6%
56436
1.4%
55407
1.3%
54424
1.4%
53412
1.3%
52443
1.4%
51423
1.4%
50450
1.5%

distance_of_shot
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct73
Distinct (%)0.3%
Missing1567
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean33.44888431
Minimum20
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size479.6 KiB
2022-02-08T15:53:19.868719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q125
median35
Q341
95-th percentile46
Maximum99
Range79
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.369656489
Coefficient of variation (CV)0.2801186551
Kurtosis0.02825934777
Mean33.44888431
Median Absolute Deviation (MAD)8
Skewness0.09989205623
Sum974366
Variance87.79046273
MonotonicityNot monotonic
2022-02-08T15:53:20.001202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
205258
17.1%
451856
 
6.0%
441433
 
4.7%
371415
 
4.6%
381341
 
4.4%
361339
 
4.4%
391299
 
4.2%
461213
 
4.0%
401144
 
3.7%
351103
 
3.6%
Other values (63)11729
38.2%
(Missing)1567
 
5.1%
ValueCountFrequency (%)
205258
17.1%
21597
 
1.9%
22541
 
1.8%
23361
 
1.2%
24350
 
1.1%
25508
 
1.7%
26611
 
2.0%
27672
 
2.2%
28640
 
2.1%
29581
 
1.9%
ValueCountFrequency (%)
991
 
< 0.1%
971
 
< 0.1%
943
< 0.1%
911
 
< 0.1%
902
< 0.1%
891
 
< 0.1%
882
< 0.1%
872
< 0.1%
851
 
< 0.1%
842
< 0.1%

is_goal
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size269.8 KiB
True
17147 
False
13550 
ValueCountFrequency (%)
True17147
55.9%
False13550
44.1%
2022-02-08T15:53:20.097820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

area_of_shot
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing1502
Missing (%)4.9%
Memory size2.2 MiB
Center(C)
12761 
Right Side Center(RC)
4562 
Right Side(R)
4370 
Left Side Center(LC)
3848 
Left Side(L)
3573 

Length

Max length21
Median length12
Mean length13.30470971
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRight Side(R)
2nd rowLeft Side(L)
3rd rowLeft Side Center(LC)
4th rowRight Side Center(RC)
5th rowCenter(C)

Common Values

ValueCountFrequency (%)
Center(C)12761
41.6%
Right Side Center(RC)4562
 
14.9%
Right Side(R)4370
 
14.2%
Left Side Center(LC)3848
 
12.5%
Left Side(L)3573
 
11.6%
Mid Ground(MG)81
 
0.3%
(Missing)1502
 
4.9%

Length

2022-02-08T15:53:20.163494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-08T15:53:20.235064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
center(c12761
23.6%
right8932
16.5%
side8410
15.6%
left7421
13.7%
center(rc4562
 
8.4%
side(r4370
 
8.1%
center(lc3848
 
7.1%
side(l3573
 
6.6%
mid81
 
0.1%
ground(mg81
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

shot_basics
Categorical

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)< 0.1%
Missing1575
Missing (%)5.1%
Memory size2.1 MiB
Mid Range
11955 
Goal Area
6787 
Penalty Spot
5321 
Goal Line
4357 
Right Corner
 
367
Other values (2)
 
335

Length

Max length15
Median length9
Mean length9.618158094
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMid Range
2nd rowMid Range
3rd rowMid Range
4th rowMid Range
5th rowGoal Area

Common Values

ValueCountFrequency (%)
Mid Range11955
38.9%
Goal Area6787
22.1%
Penalty Spot5321
17.3%
Goal Line4357
 
14.2%
Right Corner367
 
1.2%
Left Corner268
 
0.9%
Mid Ground Line67
 
0.2%
(Missing)1575
 
5.1%

Length

2022-02-08T15:53:20.325746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-08T15:53:20.397033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
mid12022
20.6%
range11955
20.5%
goal11144
19.1%
area6787
11.6%
penalty5321
9.1%
spot5321
9.1%
line4424
 
7.6%
corner635
 
1.1%
right367
 
0.6%
left268
 
0.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

range_of_shot
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)< 0.1%
Missing1564
Missing (%)5.1%
Memory size2.1 MiB
Less Than 8 ft.
8933 
16-24 ft.
7892 
8-16 ft.
6290 
24+ ft.
5937 
Back Court Shot
 
81

Length

Max length15
Median length9
Mean length10.23296605
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16-24 ft.
2nd row8-16 ft.
3rd row16-24 ft.
4th row16-24 ft.
5th rowLess Than 8 ft.

Common Values

ValueCountFrequency (%)
Less Than 8 ft.8933
29.1%
16-24 ft.7892
25.7%
8-16 ft.6290
20.5%
24+ ft.5937
19.3%
Back Court Shot81
 
0.3%
(Missing)1564
 
5.1%

Length

2022-02-08T15:53:20.494682image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-08T15:53:20.569934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ft29052
38.1%
less8933
 
11.7%
than8933
 
11.7%
88933
 
11.7%
16-247892
 
10.4%
8-166290
 
8.3%
245937
 
7.8%
back81
 
0.1%
court81
 
0.1%
shot81
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

home_away
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct74
Distinct (%)0.3%
Missing1497
Missing (%)4.9%
Memory size2.2 MiB
MANU @ SAS
 
971
MANU vs. SAS
 
890
MANU @ SAC
 
845
MANU @ DEN
 
832
MANU vs. HOU
 
828
Other values (69)
24834 

Length

Max length12
Median length10
Mean length10.97335616
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMANU @ POR
2nd rowMANU @ POR
3rd rowMANU @ POR
4th rowMANU @ POR
5th rowMANU @ POR

Common Values

ValueCountFrequency (%)
MANU @ SAS971
 
3.2%
MANU vs. SAS890
 
2.9%
MANU @ SAC845
 
2.8%
MANU @ DEN832
 
2.7%
MANU vs. HOU828
 
2.7%
MANU @ PHX825
 
2.7%
MANU vs. PHX805
 
2.6%
MANU @ HOU756
 
2.5%
MANU @ POR755
 
2.5%
MANU vs. DEN729
 
2.4%
Other values (64)20964
68.3%
(Missing)1497
 
4.9%

Length

2022-02-08T15:53:20.660102image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
manu29200
33.3%
14989
17.1%
vs14211
16.2%
sas1861
 
2.1%
phx1630
 
1.9%
hou1584
 
1.8%
sac1563
 
1.8%
den1561
 
1.8%
por1473
 
1.7%
min1403
 
1.6%
Other values (31)18125
20.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

lat_lng
Categorical

HIGH CORRELATION
MISSING

Distinct38
Distinct (%)0.1%
Missing1565
Missing (%)5.1%
Memory size2.5 MiB
42.982923, -71.446094
14171 
29.444994, -98.524120
 
975
38.567296, -121.456638
 
843
39.739968, -104.954013
 
826
33.552026, -112.071667
 
817
Other values (33)
11500 

Length

Max length22
Median length21
Mean length21.19802966
Min length21

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row45.539131, -122.651648
2nd row45.539131, -122.651648
3rd row45.539131, -122.651648
4th row45.539131, -122.651648
5th row45.539131, -122.651648

Common Values

ValueCountFrequency (%)
42.982923, -71.44609414171
46.2%
29.444994, -98.524120975
 
3.2%
38.567296, -121.456638843
 
2.7%
39.739968, -104.954013826
 
2.7%
33.552026, -112.071667817
 
2.7%
45.539131, -122.651648744
 
2.4%
29.740325, -95.365762741
 
2.4%
46.667324, -94.419250706
 
2.3%
40.774891, -111.930790702
 
2.3%
37.754130, -122.437947654
 
2.1%
Other values (28)7953
25.9%
(Missing)1565
 
5.1%

Length

2022-02-08T15:53:20.762264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
42.98292314171
24.3%
71.44609414171
24.3%
29.444994975
 
1.7%
98.524120975
 
1.7%
38.567296843
 
1.4%
121.456638843
 
1.4%
39.739968826
 
1.4%
104.954013826
 
1.4%
33.552026817
 
1.4%
112.071667817
 
1.4%
Other values (66)23000
39.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

type_of_shot
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct57
Distinct (%)0.4%
Missing15280
Missing (%)49.8%
Memory size1.7 MiB
shot - 39
1445 
shot - 36
1292 
shot - 4
1129 
shot - 15
 
701
shot - 38
 
676
Other values (52)
10174 

Length

Max length9
Median length9
Mean length8.83440358
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowshot - 30
2nd rowshot - 45
3rd rowshot - 25
4th rowshot - 17
5th rowshot - 36

Common Values

ValueCountFrequency (%)
shot - 391445
 
4.7%
shot - 361292
 
4.2%
shot - 41129
 
3.7%
shot - 15701
 
2.3%
shot - 38676
 
2.2%
shot - 44609
 
2.0%
shot - 43400
 
1.3%
shot - 17382
 
1.2%
shot - 12361
 
1.2%
shot - 52338
 
1.1%
Other values (47)8084
26.3%
(Missing)15280
49.8%

Length

2022-02-08T15:53:20.856371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
shot15417
33.3%
15417
33.3%
391445
 
3.1%
361292
 
2.8%
41129
 
2.4%
15701
 
1.5%
38676
 
1.5%
44609
 
1.3%
43400
 
0.9%
17382
 
0.8%
Other values (49)8783
19.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

type_of_combined_shot
Categorical

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)< 0.1%
Missing15417
Missing (%)50.2%
Memory size1.7 MiB
shot - 3
11685 
shot - 4
2736 
shot - 1
 
609
shot - 5
 
90
shot - 2
 
82

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowshot - 3
2nd rowshot - 1
3rd rowshot - 4
4th rowshot - 3
5th rowshot - 3

Common Values

ValueCountFrequency (%)
shot - 311685
38.1%
shot - 42736
 
8.9%
shot - 1609
 
2.0%
shot - 590
 
0.3%
shot - 282
 
0.3%
shot - 078
 
0.3%
(Missing)15417
50.2%

Length

2022-02-08T15:53:20.953997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-08T15:53:21.026128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
shot15280
33.3%
15280
33.3%
311685
25.5%
42736
 
6.0%
1609
 
1.3%
590
 
0.2%
282
 
0.2%
078
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

remaining_min_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct291
Distinct (%)1.0%
Missing1535
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean18.20461483
Minimum0
Maximum128.7616
Zeros2921
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size479.6 KiB
2022-02-08T15:53:21.125958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q311
95-th percentile95.64
Maximum128.7616
Range128.7616
Interquartile range (IQR)8

Descriptive statistics

Standard deviation29.41697273
Coefficient of variation (CV)1.61590745
Kurtosis2.959896061
Mean18.20461483
Median Absolute Deviation (MAD)4
Skewness2.051408675
Sum530882.9776
Variance865.3582846
MonotonicityNot monotonic
2022-02-08T15:53:21.262693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02921
 
9.5%
42179
 
7.1%
32154
 
7.0%
22134
 
7.0%
12061
 
6.7%
51976
 
6.4%
61758
 
5.7%
91650
 
5.4%
71646
 
5.4%
81639
 
5.3%
Other values (281)9044
29.5%
ValueCountFrequency (%)
02921
9.5%
12061
6.7%
22134
7.0%
32154
7.0%
42179
7.1%
51976
6.4%
61758
5.7%
71646
5.4%
81639
5.3%
91650
5.4%
ValueCountFrequency (%)
128.76164
 
< 0.1%
127.76161
 
< 0.1%
126.76162
 
< 0.1%
124.76164
 
< 0.1%
120.76161
 
< 0.1%
119.76163
 
< 0.1%
119.6450
0.2%
118.76163
 
< 0.1%
118.6449
0.2%
117.76163
 
< 0.1%

power_of_shot_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct206
Distinct (%)0.7%
Missing1539
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean15.99410934
Minimum1
Maximum118.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size479.6 KiB
2022-02-08T15:53:21.399004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile94.36
Maximum118.36
Range117.36
Interquartile range (IQR)2

Descriptive statistics

Standard deviation29.67681505
Coefficient of variation (CV)1.855484068
Kurtosis3.006135407
Mean15.99410934
Median Absolute Deviation (MAD)1
Skewness2.085485068
Sum466356.24
Variance880.7133514
MonotonicityNot monotonic
2022-02-08T15:53:21.532809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36268
20.4%
16085
19.8%
45485
17.9%
25093
16.6%
5263
 
0.9%
95.3675
 
0.2%
83.3671
 
0.2%
80.3669
 
0.2%
30.3668
 
0.2%
72.3668
 
0.2%
Other values (196)5613
18.3%
(Missing)1539
 
5.0%
ValueCountFrequency (%)
16085
19.8%
25093
16.6%
36268
20.4%
45485
17.9%
5263
 
0.9%
631
 
0.1%
76
 
< 0.1%
13.66
 
< 0.1%
14.65
 
< 0.1%
15.61
 
< 0.1%
ValueCountFrequency (%)
118.3650
0.2%
117.3660
0.2%
116.3659
0.2%
115.3656
0.2%
114.3660
0.2%
113.3658
0.2%
112.63
 
< 0.1%
112.3666
0.2%
111.64
 
< 0.1%
111.3649
0.2%

knockout_match_1
Real number (ℝ≥0)

MISSING
ZEROS

Distinct382
Distinct (%)1.3%
Missing1493
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean16.59940165
Minimum0
Maximum141.35232
Zeros19831
Zeros (%)64.6%
Negative0
Negative (%)0.0%
Memory size479.6 KiB
2022-02-08T15:53:21.674112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile105.8
Maximum141.35232
Range141.35232
Interquartile range (IQR)1

Descriptive statistics

Standard deviation35.17201575
Coefficient of variation (CV)2.118872505
Kurtosis2.474602919
Mean16.59940165
Median Absolute Deviation (MAD)0
Skewness1.966943168
Sum484768.9258
Variance1237.070692
MonotonicityNot monotonic
2022-02-08T15:53:21.805125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019831
64.6%
13382
 
11.0%
87.60838
 
0.1%
132.60833
 
0.1%
38.60831
 
0.1%
93.60830
 
0.1%
129.60830
 
0.1%
95.60830
 
0.1%
40.60830
 
0.1%
47.829
 
0.1%
Other values (372)5740
 
18.7%
(Missing)1493
 
4.9%
ValueCountFrequency (%)
019831
64.6%
13382
 
11.0%
17.816
 
0.1%
18.815
 
< 0.1%
19.824
 
0.1%
20.815
 
< 0.1%
21.813
 
< 0.1%
22.818
 
0.1%
23.821
 
0.1%
24.812
 
< 0.1%
ValueCountFrequency (%)
141.352324
 
< 0.1%
140.352323
 
< 0.1%
139.352321
 
< 0.1%
137.60827
0.1%
137.352326
 
< 0.1%
136.60822
0.1%
136.352321
 
< 0.1%
135.60820
0.1%
135.352323
 
< 0.1%
134.60821
0.1%

remaining_sec_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct354
Distinct (%)1.2%
Missing1539
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean39.02730256
Minimum0
Maximum144.7856
Zeros747
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size479.6 KiB
2022-02-08T15:53:21.937295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q117
median35
Q352
95-th percentile106.2
Maximum144.7856
Range144.7856
Interquartile range (IQR)35

Descriptive statistics

Standard deviation29.83528353
Coefficient of variation (CV)0.7644720895
Kurtosis1.399632538
Mean39.02730256
Median Absolute Deviation (MAD)17
Skewness1.190248182
Sum1137958.088
Variance890.1441435
MonotonicityNot monotonic
2022-02-08T15:53:22.071635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0747
 
2.4%
1509
 
1.7%
28445
 
1.4%
2442
 
1.4%
4434
 
1.4%
41423
 
1.4%
5410
 
1.3%
36410
 
1.3%
44409
 
1.3%
24407
 
1.3%
Other values (344)24522
79.9%
(Missing)1539
 
5.0%
ValueCountFrequency (%)
0747
2.4%
1509
1.7%
2442
1.4%
3398
1.3%
4434
1.4%
5410
1.3%
6368
1.2%
7372
1.2%
8383
1.2%
9350
1.1%
ValueCountFrequency (%)
144.785626
0.1%
143.785617
0.1%
143.72161
 
< 0.1%
142.785617
0.1%
142.72167
 
< 0.1%
141.785625
0.1%
141.72163
 
< 0.1%
140.785629
0.1%
140.72161
 
< 0.1%
139.785631
0.1%

distance_of_shot_1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct271
Distinct (%)0.9%
Missing1568
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean38.8018519
Minimum9.4
Maximum115.728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size479.6 KiB
2022-02-08T15:53:22.212255image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum9.4
5-th percentile20
Q126
median36
Q344
95-th percentile85.4
Maximum115.728
Range106.328
Interquartile range (IQR)18

Descriptive statistics

Standard deviation18.78771057
Coefficient of variation (CV)0.4841962341
Kurtosis3.317907443
Mean38.8018519
Median Absolute Deviation (MAD)9
Skewness1.734477383
Sum1130259.144
Variance352.9780683
MonotonicityNot monotonic
2022-02-08T15:53:22.343718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204232
 
13.8%
451511
 
4.9%
441162
 
3.8%
371125
 
3.7%
381086
 
3.5%
361036
 
3.4%
391019
 
3.3%
46965
 
3.1%
40931
 
3.0%
35884
 
2.9%
Other values (261)15178
49.4%
(Missing)1568
 
5.1%
ValueCountFrequency (%)
9.453
0.2%
10.442
0.1%
11.442
0.1%
12.444
0.1%
13.443
0.1%
14.447
0.2%
15.448
0.2%
16.464
0.2%
16.72813
 
< 0.1%
17.452
0.2%
ValueCountFrequency (%)
115.72811
 
< 0.1%
114.72810
 
< 0.1%
113.7288
 
< 0.1%
112.72810
 
< 0.1%
111.7289
 
< 0.1%
110.72810
 
< 0.1%
109.72811
 
< 0.1%
108.7286
 
< 0.1%
108.460
0.2%
107.72812
 
< 0.1%

Interactions

2022-02-08T15:53:15.226491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:00.875835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:02.143184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:03.394679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:04.644056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:05.930144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:07.224486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:08.985701image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:10.568761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:12.607537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:13.914009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:15.342112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:00.982414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:02.247777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:03.501774image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:04.758280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:06.038596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:07.339832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:09.096793image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:10.700946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:12.719754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:14.030430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:15.445891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:01.086716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:02.349258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:03.609038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:04.865556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:06.148039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:07.451369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:09.207021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:10.967378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:12.838297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:14.143127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:15.556192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:01.200223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:02.462023image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:03.723203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:04.982435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:06.260166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:07.575388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:09.319842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:11.156120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:12.954334image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:14.255374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:15.673557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:01.312067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:02.574684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:03.836731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:05.101438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:06.380790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:07.701225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:09.436105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:11.374471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:13.069827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:14.378848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:15.791094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:01.425427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:02.691874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:03.950848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:05.218795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:06.497496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:07.827844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:09.554690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:11.606949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:13.187490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:14.495844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:15.916266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:01.550924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:02.811333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:04.073380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:05.341820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:06.619509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:08.359676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:09.678627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:11.756943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:13.317115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:14.623882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:16.032403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:01.677065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:02.925267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:04.185681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:05.455630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:06.741166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:08.484267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:09.795138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:11.884262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:13.433330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:14.746615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:16.150171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:01.793568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:03.046546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:04.297163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:05.569952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:06.859505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:08.604833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:09.965562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:12.018606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:13.551082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:14.865794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:16.272114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:01.914609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:03.161248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:04.413816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:05.692272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:06.981515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:08.736254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:10.207413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:12.272607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:13.674825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:14.985564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:16.391825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:02.029301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:03.280968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:04.532424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:05.816346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:07.106138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:08.862980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:10.451933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:12.437734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:13.794358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-08T15:53:15.106179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-08T15:53:22.456339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-08T15:53:22.619220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-08T15:53:22.787594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-08T15:53:22.961975image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-08T15:53:16.626110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-08T15:53:17.037327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-08T15:53:17.699679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-08T15:53:17.955631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

location_xlocation_yremaining_minpower_of_shotremaining_secdistance_of_shotis_goalarea_of_shotshot_basicsrange_of_shothome_awaylat_lngtype_of_shottype_of_combined_shotremaining_min_1power_of_shot_1knockout_match_1remaining_sec_1distance_of_shot_1
0167.072.010.01.027.038.0TrueRight Side(R)Mid Range16-24 ft.MANU @ POR45.539131, -122.651648shot - 30NaN10.001.050.60854.200038.0
1-157.00.010.01.022.035.0FalseLeft Side(L)Mid Range8-16 ft.MANU @ POR45.539131, -122.651648shot - 45NaN10.001.028.80022.000035.0
2-101.0135.07.01.045.036.0TrueLeft Side Center(LC)Mid Range16-24 ft.NaN45.539131, -122.651648shot - 25NaN92.641.00.00063.721654.4
3138.0175.06.01.052.042.0FalseRight Side Center(RC)Mid Range16-24 ft.MANU @ POR45.539131, -122.651648NaNshot - 3NaN1.0122.60852.000042.0
40.00.0NaN2.019.020.0TrueCenter(C)Goal AreaLess Than 8 ft.MANU @ POR45.539131, -122.651648NaNshot - 142.642.00.00019.000020.0
5-145.0-11.09.03.032.034.0FalseLeft Side(L)Mid Range8-16 ft.MANU @ POR45.539131, -122.651648shot - 17NaN9.003.00.000NaN34.0
60.00.08.0NaN52.020.0TrueCenter(C)Goal AreaLess Than 8 ft.MANU @ POR45.539131, -122.651648NaNshot - 48.003.00.000112.200089.4
71.028.08.03.05.022.0TrueCenter(C)Goal AreaLess Than 8 ft.MANU @ POR45.539131, -122.651648NaNshot - 368.643.00.0005.000022.0
8-65.0NaN6.03.012.032.0TrueLeft Side(L)Goal Line8-16 ft.MANU @ POR45.539131, -122.651648shot - 36NaN6.003.00.00012.000032.0
9-33.0NaN3.03.036.032.0FalseCenter(C)Goal Line8-16 ft.MANU @ POR45.539131, -122.651648shot - 44NaN3.003.00.00052.2000NaN

Last rows

location_xlocation_yremaining_minpower_of_shotremaining_secdistance_of_shotis_goalarea_of_shotshot_basicsrange_of_shothome_awaylat_lngtype_of_shottype_of_combined_shotremaining_min_1power_of_shot_1knockout_match_1remaining_sec_1distance_of_shot_1
3068740.0100.03.03.018.030.0TrueCenter(C)Goal Line8-16 ft.NaN42.982923, -71.446094shot - 54NaN3.003.001.018.000030.000
30688-126.061.01.03.07.033.0TrueLeft Side(L)Mid Range8-16 ft.MANU vs. IND42.982923, -71.446094NaNshot - 337.64NaN1.07.000016.400
30689-12.0679.00.03.00.087.0FalseMid Ground(MG)Mid Ground LineBack Court ShotMANU vs. IND42.982923, -71.446094NaNshot - 3NaN31.601.00.000045.728
30690-113.0100.011.04.037.035.0FalseLeft Side(L)Mid Range8-16 ft.MANU vs. IND42.982923, -71.446094NaNshot - 311.0023.361.037.000035.000
306910.00.0NaN4.04.020.0FalseCenter(C)Goal AreaLess Than 8 ft.MANU vs. IND42.982923, -71.446094shot - 4NaN7.004.0027.859.785620.000
306921.048.06.04.05.024.0FalseCenter(C)NaNLess Than 8 ft.MANU vs. IND42.982923, -71.446094shot - 1NaN17.204.001.05.000024.000
306930.00.06.04.05.020.0TrueCenter(C)Goal AreaLess Than 8 ft.MANU vs. IND42.982923, -71.446094shot - 49NaN6.0064.361.05.000020.000
30694-134.0166.03.04.028.041.0TrueLeft Side Center(LC)Mid Range16-24 ft.MANU vs. INDNaNNaNshot - 33.004.001.028.000041.000
3069531.0267.02.04.010.046.0FalseCenter(C)Penalty SpotNaNMANU vs. IND42.982923, -71.446094shot - 26NaN2.00112.361.010.000046.000
306961.0NaN0.04.039.027.0FalseCenter(C)Goal LineLess Than 8 ft.MANU vs. IND42.982923, -71.446094shot - 45NaN0.004.001.039.000027.000